Systems and methods using artificial intelligence for routing electric vehicles

ABSTRACT

The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.

BACKGROUND OF INVENTION

Concerns over the impact of the increasing use of fossil fuels on theenvironment have led to multiple initiatives to provide electricvehicles (EVs) for many modes of automotive transportation. Criticalconsiderations include the design and implementation of EV automotivedrive trains, battery technology suitable for powering EVs, technologyfor charging such batteries, and the impact of widespread use of EV's onpower generation and distribution of power necessary to meet the demandthat increased use of EV's will present. Another important considerationis the management of EV traffic flow on roadways and highways to ensureacceptable performance of automotive transportation with increased EVusage.

It has been estimated that the worldwide use of EV's reached around700,000 in 2015 with 275,000 EV's in the United States. Commercialmodels include the Nissan LEAF and Chevrolet Volt. An important goal ofEV programs is a reduction of air pollution caused by fossil fueltransportation means. EV offers several advantages, including lower CO₂emissions, low petroleum usage and lower operating noise.

The price paid for these advantages is decreased automotive operatingrange. It has been reported that pure electric vehicles powered only bybattery have a range of up to about 100 miles. Plug-in hybrid electricvehicles have a battery range of about 10 miles, but revert to astandard internal combustion engine when that range is reached. Extendedrange electric vehicles have a battery range of about 50 miles andinclude internal combustion engine driven generator to increase toincrease that range. See, e.g., T. Denton, “Electric and HybridVehicles,” Routledge, 2016.

This limited driving range is a particular concern sometimes referred toas “range anxiety.” Drivers are concerned that they may not have enoughstored energy to reach their destination or even to carry out every dayroutine driving to and from multiple locations.

Lithium-ion technology is currently the preferred battery technology forEV's. Lithium-ion batteries have been the battery of choice for manyconsumer electronic products, including mobile cell phones, laptopcomputers and tablets. The automotive application is particularlychallenging requiring system control technology that ensures safeoperation and mechanical design to ensure proper operation in thehostile automotive environment. Thermal design considerations areimportant to keep operation within specified temperature ranges. See,id., and, e.g., T Horiba, “Lithium-Ion Battery Systems,” Proceeding ofthe IEEE, June 2014, pp. 939-950.

Clearly, extending the range of EV's requires systems and methods forrecharging or replacing of the vehicle batteries. Multipleconsiderations are involved and various alternatives exist for suchcharging. Most EV's are charged at home. Businesses may also offercharging stations for employees and/or visitors. Public chargingstations along road ways are also being considered and in some casesimplemented. AC charging is the standard charging method. Chargers maybe based on single phase AC (alternating current), three phase AC orhigher power DC (direct current) technology. Charging time for a 100-kmrange for lower power single phase AC systems has been reported at 6-8hours. More powerful three phase AC systems may provide comparablecharging in 20-30 minutes. High power DC systems may provide suchcharging in as little as 10 minutes. Multiple charging cableconfigurations have been standardized by the IEC (InternationalElectrotechnical Commission). See, e.g., T. Denton, “Electric and HybridVehicles,” Routledge, 2016, pp. 107-110.

Another potential technology for EV battery charging is Wireless PowerTransfer (WPT). Possible implementations include stationary WPT wherethe vehicle is parked and dynamic WPT for use along roadways when thevehicle is in motion. WPT relies upon magnetic induction and requires nocabling between the vehicle and the WPT charging mechanism. Charging isaccomplished from a fixed or roadside primary coil to a secondary coilof a stationary or moving vehicle. See Id. pp. 116-122; see also, N.Shinohara, “Wireless Power Transfer via Radio Waves,” John Wiley andSons, 2014; see also V. Prasanth, et. al. “Green Energy based InductiveSelf-Healing Highways of the Future,” IEEE TransportationElectrification Conference and Expo (ITEC), 2016.

An important new development in automotive vehicle transportation isthat of autonomous or driverless cars. Such driverless or self-drivingcars are capable of sensing their environment and navigating withlimited and sometimes no human driver control. Driverless cars make useof various technologies for sensing roadways, obstacles, traffic controlsignals, signage and other vehicles that may share a roadway beingtraveled. While such driverless vehicles are just now being introduced,predictions are that this mode of transportation will grow in the nearfuture. EV driverless vehicles may require special considerations whenchoosing routes of travel to avoid more challenging roadways orcongestion that may present difficult or more challenging sensory issuesfor the vehicle. Appropriate routes of travel for vehicles with driversmay not be appropriate for driverless vehicles. At the same time, thesystems and methods of the present invention are applicable to suchdriverless vehicles with appropriate databases and navigation programsthat account for the safety requirements of such vehicles.

The critical needs for improved systems and methods for managingcharging of electric vehicles has led to various technologicalsuggestions for allocation and placement of charging stations,integration with navigation systems, the use of Wireless Power Transfer(WPT), and the use of mathematical modeling of system design andoperation. In addition to the above citations, exemplary prior artsystems and methods attempting to address certain aspects these needsinclude the following:

-   -   1. Fouad Baouche, et. al., “Efficient Allocation of Electric        Vehicles Charging Stations: Optimization Model and Application        to a Dense Urban Network,” IEEE Intelligent Transportation        Systems Magazine, Fall 2014. This paper addresses the problem of        optimizing the location of electric vehicle charging stations in        a particular area such as the Lyon, France metropolitan area.        The model purportedly includes trip OD mileage, vehicle energy        consumption, and routing tools with elevation information        parameters as inputs to an integer linear optimization program        for the location of charging stations.    -   2. Jyun-Yan Yang, et. al., “Electric Vehicle Navigation System        Based on Power Consumption,” IEEE Transactions on Vehicular        Technology, 2015. This paper purportedly describes an electric        vehicle navigation system (EVNS) whose architecture is based on        autonomic computing and hierarchical architecture proposed to        improve the growing complexity of navigation systems. The        electric vehicle sends the traffic information center (TIC)        aggregated traffic information during a trip or a navigation        request at the start of its travel. The TIC processes the        traffic information and plans routes. The electric vehicle        receives a suggested route that guides the driver. Traffic        information, including state of charge (SOC), traffic flow,        average speed, travel time, and vehicle route, is provided by        the navigation systems.    -   3. Sepideh Pourazarm, et. al., “Optimal Routing of Electric        Vehicles in Networks with Charging Nodes: A Dynamic Programming        Approach,” IEEE Electronic Vehicle Conference, 2014. This paper        purportedly seeks to minimize the total elapsed time for        vehicles to reach their destinations considering both traveling        and recharging times at nodes using a dynamic programming        approach when the vehicles do not have adequate energy for the        entire journey.    -   4. Venugopal Prasanth, et. al. “Green Energy based Inductive        Self-Healing Highways of the Future,” IEEE Transportation        Electrification Conference and Expo (ITEC), 2016. This paper        investigates the use of Inductive Power Transfer (IPT) for        recharging electric vehicles. The use of solar and wind energy        to power such systems is discussed.    -   5. F. Tianheng, et. al., “A Supervisory Control Strategy for        Plug-In Hybrid Electric Vehicles Based on Energy Demand        Prediction and Route Preview,” IEEE Transactions on Vehicular        Technology, May 2015, pp. 1691-1700. This paper purportedly        presents a supervisory control strategy for plug-in hybrid        electric vehicles based on energy demand prediction and route        preview. A neural network is used to predict the energy demand        of the vehicle and an adaptive equivalent consumption        minimization strategy is used to optimally distribute energy        between the engine and the motor to achieve an optimal torque        split.    -   6. U.S. Pat. No. 6,487,477, J. T Woestman, et. al. “Strategy to        use an on-board navigation system for electric and hybrid        electric vehicle energy management,” Assignee—Ford Global        Technologies, Inc., Nov. 26, 2002. This patent purportedly        integrates an on-board navigation system to provide energy        management for an electric vehicle (EV) and a hybrid electric        vehicle (HEV). The vehicle location is continuously monitored,        expectations of driver demand are determined, and vehicle        accommodations are made. The system can be configured to include        location data on road patterns, geography with date and time,        altitude changes, speed limits, driving patterns of a vehicle        driver, and weather. The vehicle accommodations can purportedly        be configured to use discrete control laws, fuzzy logic, or        neural networks.    -   7. U.S. Pat. No. 9,103,686, B. Pettersson, “Method and        guidance-unit for guiding battery-operated transportation means        to reconditioning stations,” Assignee—LEICA GEOSYSTEMS AG, Aug.        11, 2015. This patent purportedly describes methods and        apparatus for guiding a mobile transportation means of a set of        transportation means to a selected reconditioning station of a        set of reconditioning stations, comprising determining a        position of the battery, determining a condition of the battery,        forecasting a consumption characteristic of the transportation        means, evaluating an achievable range of mobility of the        transportation means, assigning the selected reconditioning        station of the set of reconditioning stations, which is located        within the range of mobility of the transportation means along a        path to a desired target and guiding the transportation means to        the selected reconditioning station. An optimization of the        assignment and/or the path is executed by a search algorithm for        assigning the set of transportation means to the set of        reconditioning stations and batteries, based on actual and/or        forecasted information about multiple entities of the sets of        transportation means, stations and batteries as well as their        conditions. In addition to the “search engine,” the '686 Patent        states: “For the optimization, certain conditions and aspects of        the influencing parameters can be comprised by a usage of        abstracted mathematical models of the underlying physical or        logical background, which can be comprised in lookup tables,        statistical, historical or forecasted data. Those models can be        overall, global models of the behavior of the whole set of        resources as well as models for subsystems such as e.g. a single        battery or engine of a transportation means. For the modeling, a        plurality of methods are known to a skilled person, as e.g.        physical models, differential equations, Fuzzy-Logic models,        logical models, statistics models, forecasting models, etc.” See        '686 Patent, 4:47-58.    -   8. U.S. Pat. No. 9,199,548, R. A. Hyde, et. al., “Communication        and control regarding electricity provider for wireless electric        vehicle electrical energy transfer,” Assignee—Elwha LLC, Dec.        1, 2015. This patent purportedly describes a computationally        implemented system and method that is designed to electronically        assess electricity provider detail information associated with        providing electrical energy to one or more electric vehicle        wireless electrical energy chargers configured for wirelessly        charging one or more electric vehicles with electrical energy        from the one or more electric vehicle wireless electrical energy        chargers to the one or more electric vehicles, the one or more        electric vehicles including one or more electric motors to        provide motive force for directionally propelling the one or        more electric vehicles.    -   9. U.S. Pat. No. 9,333,873, K. Mori, et. al., “Electric motor        vehicle management system,” Assignee—Mitsubishi Electric        Corporation, May 10, 2016. This patent purportedly describes an        electric motor vehicle management system with a portable        terminal that is owned by a user and is located in an electric        motor vehicle and transmits vehicle condition information of the        electric motor vehicle including position information of the        portable terminal that has been detected by a position detector        of the portable terminal to a vehicle condition receiver of an        energy management system (EMS) installed in a customer. A        battery charging-and-discharging plan creating unit of the EMS        creates a charging and discharging plan for a battery through        the use of the vehicle condition information of the electric        motor vehicle. A charging and discharging device performs at        least one of charging and discharging of the battery of the        electric motor vehicle in accordance with the battery        charging-and-discharging plan for the battery.    -   10. U.S. Pat. No. 9,335,179, A. Penilla, et. al., “Systems for        providing electric vehicles data to enable access to charge        stations,” May 10, 2016. This patent purportedly describes a        cloud system for interfacing with an electric vehicle, wherein        the electric vehicle has a battery that is rechargeable. The        electric vehicle further has an on-board computer and a wireless        communication system that is interfaced with the on-board        computer. The on-board computer is configured to monitor a        charge level of the battery and display the level on a display        screen of the electric vehicle. The electric vehicle has global        positioning system (GPS) logic for identifying geo-location of        the electric vehicle. The cloud system is configured to manage a        user account for the electric vehicle and store data associated        with the user account. The data includes information regarding        charge parameters received from the user. The cloud system is        thus configured to interface with on-board computer of the        electric vehicle via the wireless communication system. The        cloud system is configured to access information regarding        charging stations that are available and send to the electric        vehicle one or more options of charge stations in response to        processing received geo-location of the electric vehicle and        received data regarding the charge level of the battery of the        electric vehicle and the charge parameters of the user. The        charge stations presented as options are located along a driving        path that is reachable before the charge level of the electric        vehicle reaches an empty state.        Additional prior art directed to technologies useful in some        embodiments of the present invention includes:    -   11. Chen, C. H., “Fuzzy Logic and Neural Network Handbook,”        McGraw-Hill, New York, 1996.    -   12. Cox, C., “The Fuzzy Systems Handbook,” Academic Press Inc.,        1994.        All of the above are incorporated herein by reference.

The above cited art demonstrates the industry recognition of theimportance of deriving optimal routes of travel for Electric Vehicles(EVs) with the goal of improving EV operational usefulness throughdetermination of preferred routes of travel wherein such preferredroutes include intermediate charging or replacement of EV batteries asrequired. What is needed and is missing in the prior art are specific,more efficient routing algorithms that may be employed in real-timewithout excessive and complex computation and that consider multiplefactors such as battery charging-replacement station locations, requiredtime of travel, roadway conditions, traffic congestion, includingcongestion for charging stations and minimization of required energyusage to travel between EV changing positions and destination locations.

SUMMARY OF INVENTION

The present inventions relate to systems and methods for routing anElectric Vehicle (EV) from a current position to a destination. Thesystems and methods comprise one or more specifically programmedcomputer machines with artificial intelligence expert system batteryenergy management and navigation route control. Battery energy and routeguidance parameter definitions, including range of parameter values andsubsets of those defined ranges, are stored in electronic memory of oneor more of the specifically programmed computer machines. Also stored inthese machines are expert system propositional logic statements definingrelationships between the battery energy parameters and route guidanceparameters based on parameter membership in said subset ranges.

In addition, the systems and methods involve storing in electronicmemory of one or more of the specifically programmed computer machines,one or more of: EV descriptive information, EV energy requirements, EVbattery specification information and EV current position and thelocation of the destination of said EV. The systems and methods involvemonitoring and storing in electronic memory of one or more specificallyprogramed computer machines the status of the EV stored battery energy.

The execution of computer program codes of one or more specificallyprogramed computer machines compares the current EV stored batteryenergy to one or more defined thresholds. If the battery energy is lessthan a selected threshold, information is transmitted from the EV to oneor more cloud or remote computer/database processing systems. Thatinformation may include one or more of: EV descriptive information, EVenergy requirements, EV battery specification information, EV storedbattery energy status, EV current GPS position and the EV destinationaddress location. Based on that information, the EV receives artificialintelligence expert system derived route guidance information for one ormore potential routes of travel from one or more of cloud or remotecomputer/database processing systems. That received information mayinclude information regarding one or more potential routes of travel forthe EV to reach one or more battery charging-replacement stations, andafter battery replenishment, to continue on to said destination.

Additional information may include information regarding one or moreroute parameters for each of the said potential routes. Particularpotential routes of travel are evaluated by one or more of thespecifically programmed computer machines with artificial intelligenceexpert system battery energy management and navigation route control.That evaluation is based at least in part on route guidance parametermembership in defined parameter subsets and artificial intelligenceexpert system propositional logic statements. A particular route isselected based on comparison of the results of the individual routeevaluations for potential routes of travel based on the above receivedinformation.

In some embodiments, the evaluation and selection of particular routesof travel are executed by one or more specifically programed computermachine located in the EV with artificial intelligence expert systembattery energy management and navigation route control.

In other embodiments, the evaluation and selection of particular routesof travel are executed by one or more specifically programed computermachines located in the EV together with specifically programedcloud-based or remote computer/data processing systems with artificialintelligence expert system battery management and navigation routecontrol.

In the systems and methods of this invention, transmitted EV descriptiveinformation may include one or more of: vehicle type, vehicle loadedweight and vehicle energy requirement history. The transmitted EVbattery specification information may include one or more of: batterytype, battery capacity, battery charging requirements, battery age andbattery charging time.

In some embodiments, the route guidance parameters defined for eachpotential route of travel may include the expected total travel timefrom the current location to the destination including intermediatebattery charging or replacement times along with the total expectedenergy required to travel from the current position to the desireddestination.

In some embodiments, EV total travel time for each potential routeincludes consideration of roadway conditions, traffic congestion,weather conditions and/or emergency traffic considerations.

In some embodiments of the present invention, the EV route guidanceinformation further includes consideration of actual or probablerequests for route guidance including battery charging-replacementstation usage from other EV's traveling within a defined radius distancefrom said EV position.

The present invention also includes application of the above EV batteryenergy management and route navigation control to autonomous ordriver-less vehicles with no required driver input for routedecision-making.

Other embodiments of the present invention involve the use of fuzzylogic calculations for battery energy management and navigation routecontrol as described above. Such fuzzy logic calculations comprisedefined of fuzzy sets with possible overlapping parameter ranges withdecisions based on calculation of degrees of membership in defined fuzzysets for particular considered route evaluation parameters.Defuzzification of multiple fuzzy logic degrees of memberships resultsin crisp numerical route selection indices for particular routesconsidered. A particular route may be selected based on comparison tothese derived crisp numerical route selection indices.

These and other features of the present inventions are described in moredetail below.

BRIEF DESCRIPTION OF THE DRAWINGS

While the present invention is amenable to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Theinventions of this disclosure are better understood in conjunction withthese drawings and detailed description of the preferred embodiments.The various hardware and software elements used to carry out theinventions are illustrated in these drawings in the form of figures,block diagrams, flowcharts and descriptive tables setting forth aspectsof the operations of the invention.

It should be understood, however, that the drawings and detaileddescriptions are not intended to limit the invention to the particularform disclosed, but on the contrary, the intention is to cover allmodifications, equivalents and alternatives falling within the spiritand scope of the present inventions as defined by the appended claims.

FIG. 1 illustrates, without limitation, an exemplary configuration of adriving situation with recharging stations benefiting from the routingand control system and methods of the present inventions.

FIG. 2 illustrates, without limitation, exemplary communications betweensatellite GPS transceivers and cloud-based control or remote computerdata processing equipment useful in the systems and methods of thepresent inventions.

FIG. 3 illustrates, without limitation, an exemplary travel mapdepicting possible alternate routes with charging stations between avehicle's current location and its destination location.

FIG. 4 illustrates, without limitation, an electric vehicle exemplarycontrol unit with sensor inputs, communication capabilities, databaseinformation and processing useful in the systems and methods of thepresent inventions.

FIG. 5 illustrates, without limitation, an exemplary cloud-based orremote computing and database control unit useful in the systems andmethods of the present inventions.

FIG. 6 illustrates, without limitation, exemplary cloud or remotecomputing database information accessible to the electronic vehiclecontrol units to optimize operations of the systems and methods of thepresent inventions.

FIG. 7 illustrates, without limitation, an exemplary artificialintelligence expert system decision matrix for ranking the advisabilityof using particular routes with varying placement of charging stations,depending on the electric vehicle energy needs and time requirements toreach the destination using the systems and methods of the presentinventions.

FIGS. 8A and 8B illustrate, without limitation, an exemplary artificialintelligence expert system flow chart depicting processing operationsfor the electric vehicle route determination systems and methods of thepresent inventions.

FIG. 9 illustrates, without limitation, exemplary artificialintelligence fuzzy logic calculations for the derivation of crispnumerical values ranking particular routes with varying placement ofcharging stations for the purpose of assisting determination of aparticular route of travel for an electric vehicle in need of charging.

FIG. 10 illustrates, without limitation, an exemplary selection based ontwo input variables of a particular route from among several potentialroutes based on the crisp numerical values of the fuzzy logiccalculations of FIG. 9 for the systems and methods of the presentinvention.

FIG. 11 illustrates, without limitation, an exemplary selection based onmultiple input variables of a particular route from among severalpotential routes based on the crisp numerical values of fuzzy logiccalculations for the systems and methods of the present invention.

FIG. 12 illustrates, without limitation, an exemplary fuzzy logicinference engine useful in the systems and methods of the presentinvention.

DETAILED DESCRIPTION

FIG. 1 illustrates, without limitation, an exemplary configuration (100)of a driving situation with recharging stations benefiting from therouting and control systems and methods of the present inventions. Anexemplary driving area (101) is depicted illustrating area roadways withautomotive traffic and charging stations (104). Automobiles may receivegeographic coordinate information, for example, from GPS satellites(102). As illustrated in the figure, communication with GPS satellitespermits triangulation calculations to determine the GPS coordinates ofindividual automotive vehicles. In other embodiments of the invention,such location information may also be derived from other sourcesincluding, for example, cellular telephone towers or other signaltransmission means permitting calculation of location coordinates. Aparticular automotive vehicle (105) is illustrated. In this particularexample, the automotive vehicle (105) is traveling to the destination(103), also illustrated in FIG. 1. The charging stations (104) may beused to replenish battery energy in the automotive vehicles traveling onthe roadways of the area (101). In some embodiments of this invention,the charging stations may also provide replacement batteries fortraveling vehicles. Such replacement stations may or may not include theelectric recharging of the vehicle battery. In addition to the chargingstations, certain of the roadways may include wireless or inductivepower transfer as described above to provide charging vehicles parked atsuch stations. Wireless or inductive power transfer may also includeroadway implemented energy transfer capability for inductive powertransfer to moving vehicles as described above and in the citedreferences.

FIG. 2 illustrates, without limitation, an exemplary configuration of asystem (200) benefiting from the routing and control system and methodsof the present inventions. The vehicle (201) communicates with GPSsatellites (202) for determination of geographic coordinates of thevehicle (201) as described above. The vehicle (201) also communicateswith cloud-based or remote computation and control capabilities viaradio link (203). The radio link (203) may be implemented with variousRF radio signal technologies including, for example, cellular telephonelinks, Wi-Fi links, Bluetooth links, or any other appropriate radiosignal implementation depending on the location of the vehicle and/ordesign choices for the systems and methods of this invention. Thecloud-based or remote computation and control capability as illustratedgraphically at (204) may include appropriate computer and/or databaseinformation (205) described more completely below and useful in theimplementation of the systems and methods of the present inventions.Such cloud-based or remote communication control capability may furthercommunicate with additional information resources and/or othercomputational and control facilities useful in the present inventions byadditional communication links (206).

FIG. 3 illustrates, without limitation, an exemplary travel map (300)depicting possible alternate routes (304) with charging stations (302)between a vehicle (303) current location and its destination location(301). Three alternate routes A, B and C between the present position ofvehicle (303) and the destination (301) are illustrated graphically inFIG. 3. In this example, it has been determined that the vehicle (303)has insufficient battery energy supply to reach the destination (301)without battery recharging or replacement. The three charging stations(302) along the routes A, B and C (304) represent possible recharginglocations within the calculated reach of the vehicle (303) based on thatvehicle's current energy supply and location. Choosing a particularroute of travel and charging station involves many considerations. Theoverall time of travel for the vehicle (303) along possible alternateroutes may be a factor depending upon the criticality of reaching thedestination (301) at particular times. Of course that time of travelwill depend upon the distance to be traveled along a particular route,traffic and roadway conditions along that route, required charging timeat the selected charging station, congestion at charging stations withother vehicles requiring battery charging or replacement, driving habitsof the driver of the vehicle (303) and possibly other complications intraveling along the selected route. Other considerations include theenergy required for the automotive vehicle (303) electric battery intraveling along a particular selected route. For example, that energywill depend upon the required travel time, the selected roadway,including changes in elevation, number of starts and stops required, andthe driving habits of the vehicle (303) driver. All of these factors andpossibly others may be considered in selection of the best route amongthe alternatives A, B and C (304) depicted in FIG. 3. An object of thepresent invention is to provide an artificial intelligence expert systemsolution to this problem as described below. Some embodiments of theinvention also include the use of fuzzy logic in selecting between thealternate routes A, B and C (304) for reaching the destination (301) asillustrated in FIG. 3.

FIG. 4 depicts, without limitation, a block diagram of possible elementsof an exemplary electric vehicle (EV) control unit (400) useful in thesystems and methods of the present inventions. The EV control unit (400)of FIG. 4 depicts a comprehensive collection of possible capabilities.It is to be understood that the EV control unit (400) of FIG. 4 and asdescribed elsewhere in this specification or in different embodiments ofthis invention may include all or a selected subset of the totalcapability of the EV control unit (400) of FIG. 4.

The processor (401) may be of any suitable configuration known to thoseof skill in the art. For example, the processor (401) may be a computer,microprocessor, a DSP (digital signal processor), or other controlcircuitry suitable for this application. In addition the processor (401)may be configured using a combination of these technologies.

As shown in FIG. 4, the EV control unit may include multipleinterconnected capabilities that may be attached to or designed as anintegral part of the hardware or software of the processor (401). Thesevarious capabilities useful in the operation of the EV control unit ofthis invention are characterized and discussed more completely below.

As indicated in FIG. 4, the EV control unit (400) may include hands-freeunit (402) permitting operation of a telecommunications device orcellular telephone in a hands-free mode. Such units may connect to atelecommunications device or cellular telephone using, for exampleconventional Bluetooth or Bluetooth Low Energy (BLE) (409), Wi-Fi (410)or other radio frequency data transceiver (408) communication links. Thehands-free unit (402) permits answering, placing and carrying on voiceor text communications via an external cellular telephone network usingvoice commands only without requiring the driver to hand-manipulate oroperate telecommunications or cellular telephone equipment whiledriving. Such hands-free communications may be important in thereduction of cognitive distractions to a driver of an EV vehicle in needof battery charging or replacement.

As also indicated in FIG. 4, the EV control unit (400) may include oneor more directional, beamforming microphone arrays (403). Suchdirectional, beamforming microphone arrays are useful in isolating andcapturing audio voice signals from individual speakers in the presenceof interfering signals from other speakers and other environmental noisesignals. For example, environmental noise signals may include audiosignals generated from other sources including other passengers, radio,automotive engine and vehicle operation and external noises such asgenerated by traffic or wind outside of the vehicle or other roadnoises. Directional beamforming microphone arrays are particularlyuseful in isolating speech signals of a desired speaker to the exclusionof other noise signals in the environment of the speaker.

As also shown in FIG. 4, the EV control unit (400) may include aspeech-to-text conversion capability (404). In some embodiments of thisinvention the speech-to-text conversion capability (404) may be used toconvert speech signals received from the directional microphone arrays(403) to text form, as well as for conversion of speech signals receivedby the EV control unit (400) from the telecommunication device orcellular telephone being used by the vehicle driver for texting. Also,in some embodiments, the speech/text conversion capability (404) may beused to convert text information or messages to speech enablingcommunicating with the driver of the motor vehicle or others in themotor vehicle in an audible, recognizable speech format. This capabilitymay be important in some embodiments for system control and providingaudible instructions or warnings to the driver and/or other occupants ofthe motor vehicle.

As also shown in FIG. 4, the EV control unit (400) may include anoptical camera (405) for capturing images of the driving environmentencountered by the driver of the EV. Optical camera (405) may be capableof taking multiple individual photos or videos. Such images will beuseful in some embodiments this invention to evaluate particular drivingconditions such as traffic congestion, accidents, roadway obstructionsor other conditions that may affect the ability or time required for theEV to reach a designated charging station and ultimately to reach thedesired destination.

The optical cameras (405) of FIG. 4 may also be used with image analysissoftware to perform such tasks as facial recognition to identifyparticular drivers. The EV control unit may be used to maintain historyfiles of motor vehicle drivers with histories of their driving habitsincluding driving tendencies that affect the efficiency of use ofavailable battery energy. Examples of such tendencies that may be ofconcern include unnecessary frequent acceleration or braking. Particulardrivers may present increased frequency of such inefficient drivinghabits. Facial recognition systems and methods of the present inventionmay include giving warnings or advice to particular drivers in anattempt to minimize inefficient driving habits. Such inefficient drivinghabits may also be reported to data collection centers for analysis andinformation. Such information may also influence the choice of the mostappropriate route from multiple possible routes as illustrated in FIG. 3and discussed above. Inefficient driving habits of particular driversmay be important considerations for some routes such as those with heavytraffic congestion, changes in altitude along a route, adverse drivingweather conditions, or other such considerations.

As further indicated in FIG. 4, the EV control unit (400) may include aGPS (Global Positioning System) receiver (406) useful for tracking thelocation and movements of the motor vehicle. The Global Position System(GPS) may make use of triangulation calculations of positions based onsignals received, for example, from multiple geostationary satellites.Such systems provide location information accurate within approximatelyone meter. Massive databases exist providing GPS coordinates forvirtually every addressable location in the United States and elsewhere.Mobile communication networks implement Home Location Registries (HLRs)and Visitor Location Registries (VLRs) providing instant locationinformation for mobile wireless devices throughout the country. Suchdatabases also provide detailed maps of highways and roadways used bymotor vehicles. Route maps and location information may be used in thepresent invention to verify that the motor vehicle is indeed travelingon established highways or roadways and further to provide markings ofthe location of such a vehicle as a function of time along those knownroutes. This information can be used in combination with accuratetime/clock information available to the EV control unit (400) of thisinvention using, for example, time/clock distribution unit (415) shownin FIG. 4. Knowing the accurate time and location of a particular EV maybe of assistance in determining the progress of that EV toward a batterycharging or replenishing station or the final destination of the EV asdepicted in FIG. 3.

EV location information may also be derived based on the EV distancefrom cellular telephone towers or other known fixed locationstransmitting signals that may be received by one or more of thereceivers of the EV control unit (400) of FIG. 4. Here again,triangulation calculations may be made using three or more such locationtransmission signals.

As also shown in FIG. 4, the EV control unit (400) may include acellular transceiver (407) used to receive and transmit cellularcommunication information between the EV control unit (400) and externalsources accessible to the cellular telephone network or atelecommunication device or cellular phone located in the motor vehicle.The cellular transceiver (407) may be used, for example, to communicatewith cloud-based computation, database management and control systems ofthe type illustrated in FIG. 2 above and discussed more completelybelow.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude a data transceiver (408) useful for communications with otherdevices in the motor vehicle including vehicle information systems,control and display systems, as well as telecommunication devices orcellular telephones or Internet/World Wide Web (WWW) connections.

Similarly, as shown in FIG. 4, the EV control unit (400) may alsoinclude a Bluetooth transceiver (409) and/or a Wi-Fi transceiver (410).Both Bluetooth and Wi-Fi transceivers are used for short-range voice anddata communications. In the present invention such transceivers may beused to communicate between EV control unit (400) and charging stationsthat are being or may be used by the EV. Such Bluetooth and Wi-Fitransceivers may also be used, for example, to communicate with nearbyvehicles present in ongoing traffic or waiting for use of particularcharging stations such as the type illustrated in FIG. 3 above.

Telecommunication transceivers, such as a cellular transceiver (407),the data transceiver (408), Bluetooth transceiver (409) and/or the Wi-Fitransceiver (410) may also be used to communicate with near fieldcommunication devices, including toll tags stations, charging stations,or other stations encounter by the EV where the transfer of informationbetween the EV and the station may be used to improve efficiency ofdriving and battery usage and also to improve navigation route selectiondepending upon particular circumstances and conditions encountered bythe EV.

In some embodiments, the EV control unit (400) of FIG. 4 may includeartificial intelligence expert system technology (411) with the goal ofimproving decisions made by the EV control unit (400). Such artificialintelligence expert system technology may prove especially beneficial inassessing the most appropriate route selection and navigation guidancefor the EV. In some embodiments of the present invention, expert systemtechnology may be used to program decision making capability based oninputs from experts with particular EV technology knowledge, batteryefficiencies and range considerations, and the impact of multiplefactors such as roadway conditions, weather conditions, trafficconditions, accidents or dangerous situations, and/or other motoristsparameters that may affect decisions and selection of the best route oftravel for the EV to reach appropriate battery charging or replenishmentstations and the ultimate destination of the EV. Inputs from suchexperts may be used to program the expert system formulations of thepresent invention. In addition, such expert system information may bedynamically changed depend upon changing environments such as driverhabits or other important EV situational considerations. As describedfurther below, in some embodiments, such expert system knowledge isconveniently set forth in propositional calculus statements withappropriate multiple parameter matrix presentation.

The artificial intelligence expert system capability (411) may alsoinclude “learning” capability, including the development of databasesrecording driving habits of particular drivers, such as driving acumenand driving tendencies that may result in more or less efficient use ofonboard battery energy in the EV. Such “learning” will result in anadaptive control system providing feedback to the EV driver and/orcontrol systems for continuous optimization of route selection in thepresence of dynamically changing EV situations.

As also indicated in FIG. 4, the EV control unit (400) may include fuzzylogic capability (412). Fuzzy logic is a method of representing analogprocesses on a digital computer. With fuzzy logic control, statementsare written in the form of the propositional calculus logic statements.These statements represent somewhat imprecise ideas reflecting thestates of the variables. Fuzzy logic is particularly appropriate when anexpert is available to specify these propositional statementscharacterizing the relationships between system variables. In thepresent invention such propositional statements and fuzzy logic may bebeneficial in analyzing the relationships between various parameterscharacterizing driving situations and responses to those situations asdescribed more completely below.

Telecommunication device or cell phone “pairing” (413) may also beincluded in the EV control unit (400) of the present invention. Such“pairing” permits a telecommunication device or cell phone to beconnected to EV control unit (400) via telecommunication links such asBluetooth, Wi-Fi or the like. With these connections, voice or datacommunication signals transmitted to and from the telecommunicationsdevice or cellular telephone may be relayed through the EV control unit(400) via the interconnecting telecommunication links. In addition, such“pairing” permits commands and responses to be communicated between atelecommunications device or cellular telephone and the EV control unit(400). One intended use of such commands would be to better inform thedriver of the EV of appropriate actions to be taken to ensure adequatebattery energy is available to reach the desired destination of the EV.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude a data-base access capability (414) connected to EV processor(401) for accessing and updating data-base information useful in theoperation of the present invention. The data-base information may bestored locally as part of the EV control unit (400), or maybe locatedremotely and accessible, for example, from the cloud or other remoteprocessing systems through the Internet, cellular telephonecommunication networks or other appropriate radio links. In someinstances, database information may also be accessed from informationstored in other control and information data files implemented in themotor vehicle such as information stored for use by vehicle informationdisplay systems. Such vehicle information display systems may includeinformation necessary for dashboard displays concerning vehicleoperational status, speed, odometer readings, engine performance,battery charge levels and warning signals. In addition, other controland information data files implemented in the motor vehicle may includefiles used to drive other on-board displays including, for example,touch screen displays or displays manipulated using point-and-click orother operator controls for navigating and selecting information to bedisplayed including, for example, navigation information and maps,vehicle status, weather, entertainment system control, telecommunicationdevice control and the like. In some embodiments of this invention,information from EV control unit (400) may in fact be displayed on suchother on-board displays or may be made available for access by the motorvehicle driver or passengers using such displays. In some embodiments ofthis invention the EV control unit (400) may be integrated into and madean operational part of other vehicle control and/or display systemsincluding, for example, the EV' s telematics unit.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude a time/clock distribution capability (415) operating to makeaccurate date and time information available to the EV control unit(400). Such information may be used, for example, in the calculation ofvehicle speed by providing elapsed time between particular vehiclelocation points along a route of travel. Such time and date informationmay also be used to create history files recording, for example, batterycharging levels, inefficient driver operation of the EV with timestamps,date and time of requests for assistance or routing information, dateand time of particular drivers being in control of the EV and other suchinformation useful in the embodiments this invention. In someembodiments such information may be reported to vehicle owners oroperators such as taxicab companies, trucking companies, rental caragencies or other equipment leasing or renting organizations employingor otherwise using drivers to operate their vehicles or equipment. Suchreported information may be used, for example, to work with drivers toimprove their driving habits, admonish drivers for inefficient drivinghabits or to take other corrective action deemed necessary foroperational cost, safety or liability concerns.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude accelerometer (416) capabilities. An accelerometer is a devicethat can measure the force of acceleration, whether caused by gravity orby movement. An accelerometer can therefore be used to measure or assistin the measurement of the speed of movement of an object to which it isattached. In the present invention, accelerometers may be used tomonitor frequent and/or unnecessary vehicle acceleration which requireincreased energy utilization from the EV battery sources. For examplesolid-state accelerometers can sense the tilt, movement and speed beingapplied to them. Useful accelerometer technology includes piezoelectric,piezoresistive, resonant, strain gauge, capacitance, tunneling, andheated liquid and gas accelerometers. Silicon MEMS accelerometers thatwork on the capacitive approach or ones that are based on temperaturedifferentials in heated-gas are useful in some embodiments of thisinvention. Such thermal accelerometers may be fabricated in monolithicstructures with integration with all the necessary signal conditioning,interface and embedded circuitry on a single integrated circuit.Accelerometers are used today in automobiles for crash detection andairbag deployment and detection of automobile rollover accidents.

A speaker unit (417) may also be included as part of the EV control unit(400). The speaker may be used to announce battery charging levels,remaining distance with the present battery charging, advice on selectedroutes, including routes that may include charging stations, projectedtime to the destination, and other such calculations made by the systemsand methods of the present invention. The speaker may also be used toinstruct the driver of particular actions to be taken to ensuresufficient battery charging and attainment of the desired destination inminimal time.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude associated memory (418) for storing software programs, vehicleinformation, measurement history information and other data useful orcollected by the EV control unit (400) in the operations of thisinvention. The associated memory (418) may comprise random access memory(RAM), read only memory (ROM), solid-state memory, disk memory, opticalmemories or any other appropriate memory technology known to those ofskill in the art. While memory unit (418) is shown in FIG. 4 is aseparate assembly, it is to be understood that some or all of suchmemory may be distributed among other various operational, control andcommunication capabilities of the EV.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude display capability (419) for displaying operational status andinformation concerning the operation and calculated results derived bythe EV control unit (400). The display (320) may be a separate displayassociated with EV control unit (400), or, alternatively, the display(419) may be integrated with an operational part of other displayspresent in the motor vehicle such as a motor vehicle telematics unit.Useful display technologies include liquid crystal displays (LCD), lightemitting diode displays (LED), plasma displays, smart glass, touchscreen displays, menu-driven displays, and displays operated usingspeech commands or other suitable display technology.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude additional input-output-device (420) capabilities. For example,standard USB ports may be used for such access. Other possibilitiesinclude the Common Flash memory Interface (CFI) approved by JEDEC andother standard memory unit interfaces. Other possibilities include audioinput/output ports, video ports such as HDMI ports and otherinput/output capabilities.

As also shown in FIG. 4, the EV control unit (400) may further includean RFID (radio frequency identification) tag device (421) used toidentify the motor vehicle and communicate information or results fromEV control unit (400) to RFID tag readers located along highwaystollways or roadways along which the motor vehicle is traveling. TheRFID tag device (421) operating with EV control unit (400) may bepowered by power supply (422) of the EV control unit (400) shown in FIG.4. Alternatively the RFID tag device (421) may be powered fromexternally generated electromagnetic energy waves emitted by an RFID tagreader. Information transmitted from the RFID tag device (421) mayinclude information indicating battery charging level and anothervehicle status information.

In addition, as shown in FIG. 4, the EV control unit (400) may furtherinclude a power supply (422) necessary for operation of the EV controlunit (400) including the various capabilities depicted in FIG. 4. Thepower supply (422) may derive energy from the vehicle electrical powersupply source or may be implemented as a separate battery or energysupply including, without limitation, solar energy, energy derived fromexternal impinging electromagnetic waves, or energy derived from motorvehicle mechanical operations such as breaking or coasting.

It is to be understood that while the EV control unit (400) of FIG. 4 isdepicted and described above as a unitary assembly, it is also possible,and in some cases desirable, that perhaps some of the operationalfeatures shown in FIG. 4 are shared and possibly implemented as part ofother automobile control, communications, processing and/or displaycapabilities such as a motor vehicle telematics unit. In addition, itshould be clear that several of the operational capabilities of the EVcontrol unit (400) of FIG. 4 may be implemented with distributed devicesand/or capabilities located throughout the motor vehicle andcommunicating with the processor (401) as indicated in FIG. 4.

It should further be understood that other embodiments of the systemsand methods of this invention may use a subset of the capabilitiesdepicted in FIG. 4 without departing from the fundamental integratedsystem and method teachings of this invention.

FIG. 5 depicts, without limitation, a block diagram of possible elementsof an exemplary electric vehicle (EV) cloud control unit (500) useful inthe systems and methods of the present inventions. The EV cloud controlunit (500) of FIG. 5 includes a comprehensive collection of possiblecapabilities. It is to be understood that the EV cloud control unit(500) of FIG. 5 and as described elsewhere in this specification or indifferent embodiments of this invention may include all or a selectedsubset of the total capability of the EV cloud control unit (500) ofFIG. 5.

The processor (501) may be of any suitable configuration known to thoseof skill in the art. For example, the processor (501) may be a computer,microprocessor, a DSP (digital signal processor), or other controlcircuitry suitable for this application. In addition the processor (501)may be configured using a combination of these technologies, includingthe use of multiple processors.

In addition, as shown in FIG. 5, the EV cloud control unit (500) mayfurther include associated memory (502) for storing software programs,vehicle information, EV history information and other data useful in theoperations of this invention. The associated memory (502) may compriserandom access memory (RAM), read only memory (ROM), solid-state memory,disk memory, optical memories or any other appropriate memory technologyknown to those of skill in the art. While memory unit (502) is shown inFIG. 5 as a separate assembly, it is to be understood that some or allof such memory may be distributed among other various operational,control, communication and/or database information storage units.

In addition, as shown in FIG. 5, the cloud control unit (500) mayfurther include display capability (503) for displaying operationalstatus and information concerning the operation and calculated resultsderived by the cloud control unit (500). The display (503) may be aseparate display associated with cloud control unit (500), or,alternatively, the display (503) may be integrated with an operationalpart of other displays. Useful display technologies include liquidcrystal displays (LCD), light emitting diode displays (LED), plasmadisplays, smart glass, touch screen displays, menu-driven displays, anddisplays operated using speech commands or other suitable displaytechnology.

As also shown in FIG. 5, the cloud control unit (500) may also includeartificial intelligence expert system capability to complement or usedin place of that described above for the EV control unit (400) of FIG. 4and described more completely below. In some embodiments, the artificialexpert system capability may also include fuzzy logic capability tofurther complement or be used in place of that described above for theEV control unit (400) of FIG. 4. For example, the artificialintelligence fuzzy logic capability (504) of FIG. 5 may include varyingfuzzy logic implementations for multiple variable inputs and variationsof such algorithms that may be useful in determining optimal routes forEVs accessing the cloud control unit (500). Using the communicationcapabilities of the cloud control unit (500), it is also possible todownload expert system artificial intelligence and fuzzy logicalgorithms as appropriate to EVs having access to the cloud control unit(500).

In addition, as shown in FIG. 5, the cloud control unit (500) mayfurther include a power supply (505) necessary for operation of thecloud control unit (500) including the various capabilities depicted inFIG. 5. The power supply (505) may derive energy from a local powersupply source or may be implemented as a separate battery or energysupply including, without limitation, solar energy, wind energy, orother green energy sources.

As also shown in FIG. 5, the cloud control unit (500) may also compriseWorld Wide Web (WWW) Internet transceivers (506). Such transceiversensure access to the cloud control unit (500) from EVs in need of routeguidance for the purpose of recharging or replenishing battery energy asdiscussed above. The World Wide Web Internet transceiver interfaces maybe accessible to all vehicles, or registration may be required to gainaccess to the routing database information available from the cloudcontrol unit (500).

As shown in FIG. 5, the EV cloud control unit (500) may include aGSM/CDMA or other cellular transceiver (507) used to receive andtransmit cellular communication information between the EV cloud controlunit (500) and external sources accessible to the cellular telephonenetwork or a telecommunication device or cellular phone located in anEV. The cellular transceiver (507) may be used, for example, tocommunicate with cloud-based computation, database management andcontrol systems as discussed more completely below.

Similarly, as shown in FIG. 5, the cloud control unit (500) may alsoinclude one or more Wi-Fi transceivers (508) and/or Bluetoothtransceivers (509). Both Bluetooth and Wi-Fi transceivers are used forshort-range voice and data communications. In the present invention suchtransceivers may be used to communicate between cloud control unit (500)and nearby computation, storage or information access devices or units.

In addition to the communication capabilities shown in FIG. 5, othercommunication links not shown including, for example, cable links, fiberoptic links, of other radio or wired connections may be used tocommunicate with the cloud control unit 500 of FIG. 5.

FIG. 6 depicts, without limitation, database elements (600) of the cloudcontrol unit (500) depicted in FIG. 5 and discussed above. The databaseelements (600) may be centrally located in a database storage unit ormay be distributed among several such units or part of databaseinformation accessible from the cloud control unit (500) of FIG. 5, orfrom EVs requiring access to such information. In the case ofdistributed database information, information indicated in FIG. 6 may bepart of database information from distributed agencies or organizationssuch as weather database information, police report information, highwayor road management agencies, traffic monitoring agencies, or agenciesresponsible for EV charger stations.

The cloud control unit EV database management unit (601) is accessibleby communication links (610) as indicated in FIG. 6. Unit (601) respondsto requests for information from the database files indicated in FIG. 6.Such information may be used, for example, to determine optimal routingfor EVs in need of battery charging or replenishment as described aboveand in more detail below.

For example, the database information (600) of FIG. 6 includesnavigation system data (602). Such information includes data descriptiveof travel routes, including location addresses along those routes andthe location of the EV battery charging stations. This informationexists today in the form of navigation system databases used by multipleservices available on the Internet to provide routes betweendestinations using GPS coordinates and/or specific address informationdescribing the coordinates of current locations and destinationlocations. The same databases used for those purposes may be used inembodiments of the present invention or separate databases devoted tooptimal routing for EVs to manage a battery charging energyconsiderations may be used.

As also indicated in FIG. 6, database information (600) may includenavigation route information for roadways or routes especiallyrecommended for driverless or autonomous vehicles. Such roadways orroutes may include special driverless or autonomous vehicle controlfeatures such as roadway markings, signs, electronic signals, nightlighting features or similar such marking or control capabilities toassist in the operation of driverless or autonomous vehicles.

In addition, the cloud control unit EV database management (600) of FIG.6 may also include traffic database (603). Such traffic databaseinformation may include information descriptive of vehicle congestion,pedestrian traffic and/or the density or number of EVs present in aspecific area. The number of EVs present may be used to alter routingdecisions based on the likelihood that multiple such EVs will be in needof battery charging or replenishment. In this case, it may be desirableto route EVs requesting charger station locations in a manner thatavoids directing multiple EVs to the same chargers, even though thosechargers may be in close proximity to multiple such EVs.

The database information of FIG. 6 may also include EV account data(604). Such account data may be obtained by registering potential usersof the invention with uploading of the information from usercommunication devices, computers, the EV manufacturer, government EVrecords or from the EV itself. Such account information may includeimportant vehicle data, including the vehicle type, the vehicle energyusage, required vehicle charging times and other important vehicleinformation useful in determining optimal routes of travel for EVs inneed of battery charging or replenishment. EV account data may includestatic data descriptive of the vehicle itself as well as data that maydynamically change with time, such as the vehicle weight, vehiclecondition, identification of vehicle driver or vehicle history filesrecording, for example, energy usage or performance history.

As also indicated in FIG. 6, the cloud control unit EV databasemanagement (600) may include battery charger-replacement station data(605). As discussed above, multiple charger implementations areavailable including single-phase AC, three-phase AC and DC chargers ofvarying power capacities and requiring different charging times. It isimportant in advising EVs with specific charging requirements that thoseEVs are directed to the appropriate charger type and a charger capableof delivering the required charge while meeting desired charger times.Charging times may be influenced by charger station status which may betemporarily out of service, or undergoing routine maintenance, or, forother reasons, not immediately available to provide chargingcapabilities. In addition, charger queues may develop with multiple EVswaiting for access to a given charger. In the case of batteryreplacement, the station database information (605) will includereplacement battery availability, types and battery interchangeabilityinformation. All these factors need may be considered when recommendingtravel routes to particular charging-replacement stations for requestingEVs.

As also indicated in FIG. 6, the cloud control unit electric vehicle,database management (600) may include weather information (606),including present weather conditions, weather forecasts and/orparticular information concerning inclement weather involving forexample, ice, snow, rain or flooding. In some cases, particular roadwaysmay be more affected by inclement weather than others. For example,roadways with multiple bridges or highway interchange overpasses may bemore affected by ice and snow than roadways without such structures.Roadways passing through lower areas susceptible to high waters orflooding should be avoided when selecting routes for EVs in need ofbattery charging or a replacement. Also, forecasted inclement weathermay influence route selection for vehicles in need of battery chargingor replacement.

Also, the database information available from the cloud control unit EVdatabase management (600) may include police or law enforcementinformation (607) providing the locations of accidents or otheremergencies, including criminal activity. Here again, such informationmay be considered when recommending routes for EVs in need of batterycharging or replenishment. Delays caused by congestion or slow trafficresulting from such emergency situations may be intolerable to an EVwith limited battery power range.

As further indicated in FIG. 6, recommending routes for EVs in need ofbattery charging or replenishment may be influenced by special events inprogress or scheduled along selected routes. Special event data (608),including the locations of such events and predicted or real traffic orcrowd congestion caused by such events may be considered. Here again, itmay be the case that such event locations should be avoided whenrecommending routes for EVs in need of battery charging orreplenishment.

Yet another consideration addressed in the database informationindicated in FIG. 6 is that of road conditions (609). Road outages, roadrepairs or other dangerous roadway situations may be considered in theevaluation of alternate routes for recommendation to EVs in need ofbattery replacement or replenishment. Here again such situations shouldgenerally be avoided. The risk of battery charge depletion resultingfrom delays when encountering difficult road situations needs to beminimized.

FIG. 7 depicts in matrix form artificial intelligence expert systemrelationships (700) between two selected parameters, energy needed andtravel time that may be used in some embodiments of the presentinvention to assist in selecting a particular route for the electricvehicle to travel from its current position to its ultimate destination.As indicated in FIG. 7 the range of the parameters for energy needed andtravel time are divided into exemplary subranges corresponding to verylow, low, medium, high and very high. For each combination of suchvalues for the two parameters being considered in FIG. 7, an artificialintelligence expert decision rating is provided indicating thedesirability of traveling along a specific specified route having thecorresponding values of the energy needed and travel time parameters.These route desirability ratings are provided by traffic managementexperts and are part of the artificial intelligence expert systemdatabase. As indicated in FIG. 7, the route desirable ratings may bedefined by such experts as, for example, being very low, low, medium,high, and very high. In the exemplary embodiment depicted in FIG. 7,twenty-five such expert system defined rules are shown.

As stated above, the cloud control unit will derive numerical values forthe energy needed and travel time parameters for each of the potentialroutes evaluated in response to the request from an individual electricvehicle. Each of those values will fall within one or more of thespecified ranges as indicated as being very low, low, medium, high orvery high. The 25 route desirability rating entries in the matrix ofFIG. 7 represent the desirability of using a particular route for agiven combination of the energy needed and travel time parameters.

While the example of FIG. 7 is limited to two variables, travel time andenergy needed parameters, clearly additional tables may be constructedto include other important variables in the decision process.Multidimensional tables may be constructed with more than two variablesto reflect additional indices. For example, a separation of travel timeinto two variables reflecting battery replenishment time and actualdriving time may be used. Other parameters may include, for example, thecondition of the vehicle, road conditions, driving impediments such asaccidents or road construction, battery condition or other suchvariables that may affect the route selection process.

The route desirability rating matrix (700) of FIG. 7 is a form ofartificial intelligence and forms the basis of an intelligent system.For example, each of the results indicated in FIG. 7 may be expressed inpropositional calculus logic form, for example, as follows:

1. If energy needed is medium and the travel time is medium then routedesirability rating entry is medium.

2. If energy needed is medium and travel time is high then the routedesirability rating entry is low.

3. If energy needed is very low and travel time is medium then the routedesirability rating entry is medium.

-   Clearly 25 such logical statements exist for the entries in FIG. 7.    For each such logical statement, a route desirability rating for the    given route may be determined by the expert system of the present    invention. The route desirability rating may be displayed on the    display (419) of FIG. 4 in various forms including text messages,    flashing alerts of various colors for various route desirability    rating entries, with audible messages from the speaker (417) of FIG.    4 or with a combination of such visual or audible alerts.

Consider, for example, the three possible routes A, B and C of FIG. 3discussed above. Each of those potential routes may be derived usingexisting routing and navigation systems including in-car systemsavailable on purchased vehicles, add on navigation systems such asTomTom and Garmin, and cell phone, tablet or other wireless devicenavigation applications (apps) such as Google Maps or Waze. The completeroute may be composed of two portions: (1) a route from the EV currentposition to a charging station, and, (2) a route from the chargingstation to the destination. The problem remains, however, to select fromamong the three potential routes based on the EV battery charging orreplenishing requirements. The expert system matrix of FIG. 7 can beused to assist in making that selection based on the required energyneeded and time required for each route of travel. Using the expertdefined matrix of FIG. 7, a route advisory index of very low, low,medium, high or very high may be derived. In some situations, one routewill have an advisory index higher than others and will be selected. Itmay also happen that more than one route will have the same advisoryindex. For example, in FIG. 7 multiple combinations of energy needed andtravel time parameters result in a route advisory index of “medium.” Insuch a case, a prioritization of energy and time requirements may beused to select the most desirable route. If travel time is morecritical, the route with the lowest travel time will be selected. Ifmultiple energy needs apply to that selected travel time, the route withthe lowest energy level corresponding to the selected lowest travel timeand will be selected. Additional parameter values may be considered ifnecessary as explained further below.

FIGS. 8A and 8B depict, without limitation, an exemplary flowchart (800)for the operation of the EV battery charging or replenishment systemsand methods of the present invention. The process is initiated at start(801) which may be activated by the driver or automatically by the EV.When initiated, information (802) is transmitted to the cloud controlunit, including, for example, information descriptive of the particularvehicle, the vehicle's location derived, for example, using GPScoordinates, battery charge level of the EV and the location orcoordinates of the EV destination. Such parameters are used to properlyevaluate the EV situation in terms of the sufficiency of battery energyavailable, the distance to the destination and the other considerationssuch as traffic congestion, roadway delays, inclement weather issues orother conditions or situations that may impact the sufficiency ofbattery energy to make the planned trip.

At (803) information indicating the battery charge requirements isreceived at the EV from the cloud control unit (500) of FIG. 5. Thebattery charge requirements will depend upon the various factorsdescribed above including vehicle and battery information, batterycharge status, distance to the destination and roadway, possible routesof travel, traffic or other factors influencing the required energy toreach the destination as described above.

At (804), a decision is made as to whether or not it will be necessaryto replenish the battery to a sufficient level necessary to travel tothe final destination. Such a determination can be made, for example, bycomparing the level of remaining battery charge energy to a thresholdvalue based, for example, on the received battery charge requirements toreach the destination at (803). If the battery charged level is belowthe threshold, replenishment of energy will be needed. If no suchreplenishment is necessary, control is returned to start (801) forcontinuous or periodic updating of the vehicle information transmissionto the cloud (802) for an evaluation of the battery charge requirementsfrom the cloud at (803). If replenishment is required, control is passedto block (805) to request an artificial intelligence derived route,including a battery charging-replacement station appropriate toreplenish the battery energy supply to a level sufficient to reach thefinal destination. In determining the suggested route, the invention ofthe present invention systems and methods may consider multiple possibleroutes with different locations of charging stations that may be inrange of the present location of the EV requesting battery charging orreplacement assistance as illustrated, for example, in FIG. 3.

Once a recommended route has been received, the EV will navigate alongthat route arriving at the selected battery charging-replacementstation, replenishing the battery supply and then continuing on to thedestination as indicated at block (807). While EV is navigating theselected route, the battery level is continuously monitored at (807) todetermine whether or not that battery energy level has been depleted tothe point where it may be necessary to replenish the battery againbefore arriving at the destination. In the case that additionalreplenishment or replacement is required, control is returned to start(801) with updating of the vehicle status information and with the abovedescribed steps being repeated to ensure sufficient EV energy ismaintained. If it is determined at (807) that the battery does not needreplenishing, the EV continues on to the destination as indicated at(808). The process ends at (809) when the destination is reached.

FIG. 8B depicts more detailed process flow evaluation of AI derivedroutes indicated at (805) and (806) of FIG. 8A. As indicated in FIG. 8B,control is passed from FIG. 8B (805) at (812). Multiple potential routessuch as routes A, B, and C of FIG. 3 are evaluated and the routes withthe highest expert system route advisory index set forth in FIG. 7 areselected. If one identified route has a higher route advisory index thanany other identified route, then at (814) control is passed through(817) for transfer of the selected route to the EV navigation system. Inthis case control is returned by connector (818.)

It may also happen the multiple candidate routes may have the sameadvisory route index as indicated in FIG. 7. For example, more than oneroute may have an advisory route index of “medium.” In this case controlis passed from decision element (814) to (815) for further evaluation.For example, priorities may be established for individual parameters tofurther resolve selection of a desired route. As indicated in (816)parameter priorities may be assigned indicating that the energy neededparameter has a higher priority than the travel time parameter. In thiscase, routes having the same advisory route index in FIG. 7 would becompared based on energy needed. The route with the lowest energyrequirement would be selected. In the event that multiple routes arestill candidates, then the route with the lowest travel time would beselected from those having the same advisory route index and energyneeded. In the event that candidate multiple routes multiple routesstill exist, then additional considerations such as traffic congestion,emergency situations or roadway considerations may be taken into accountto finally resolve the best route. In the event the multiple routesstill exist that are basically equivalent, then one of those routes willbe selected and passed to the EV navigation system as the recommendedroute to be traveled.

As explained above in FIGS. 7, 8A and 8B, routing options may becontinually evaluated based on information provided from the electricvehicle to the cloud-based control unit used to select appropriateroutes for electric vehicles under its control. As also explained above,the electric vehicle provides current location information for thevehicle and destination location information to the cloud-based controlunit. Such location information may be in the form of GPS coordinatesderived from satellite GPS systems as described above, or other suitablelocation information such as physical addresses of an electric vehicle,battery replenishment stations and changing destination information. Theelectric vehicle also supplies current battery charge levels to thecloud-based control unit. The electric vehicle also supplies informationdescribing the electric vehicle's energy requirements, for example, inrequired kilowatt-hours per mile traveled. Clearly, such requirementswill vary based on the type of vehicle such as a small electric vehicleto larger, heavier vehicles such as buses or trucks. Route guidanceinformation may be derived from such dynamically changing informationusing present-day navigation systems known to those of skill in the artas discussed above.

Using the current vehicle location and destination location information,the cloud-based control unit may determine the travel time from thecurrent location to the destination using present-day navigation systemsknown to those of skill in the art as discussed above. If it isdetermined that it is necessary to charge or replace the electricbattery in the vehicle to have sufficient energy to reach thedestination, the cloud control unit will evaluate multiple routes oftravel that stop at different distributed charging stations in thecurrent vicinity of the electric vehicle as described above. Clearly thedistance traveled by the vehicle in reaching the destination may varyfor each of the evaluated routes. Furthermore, the energy required totravel the various routes may vary depending upon the distance to betraveled and other factors such as variations in altitude along suchroutes, corresponding to hills or valleys to be traversed, trafficrequirements along such routes that may result in travel delays or otherimpediments that may delay the electric vehicle such as accidents,crowds, road construction, etc. In addition to driving time, the totaltravel time may include time waiting in a queue at an individualcharging or battery replacement station for others arriving ahead of anindividual traveler to make use of the station. In addition, the totaltraveling time may include the actual time required to charge or replacethe battery of the electric vehicle being considered. In someembodiments, it may be desirable to only provide replenishment energy tothe electric vehicle battery sufficient to reach the destination or toreach the destination with possibly some additional energy margin toprovide high confidence that sufficient battery energy has been suppliedto reach the destination. The cloud control unit will derive numericalvalues for the energy needed and travel time parameters for each of thepotential routes evaluated in response to the request from an individualelectric vehicle.

In some embodiments, the EV route guidance information may furtherinclude consideration of actual or probable requests for route guidanceincluding battery charging or replacement station usage from other EVstraveling within a defined radius or distance from said EV position.Such information may be gathered from said other EV's that may affectthe expected waiting times or queues that may be encountered at batterycharging or battery replacement stations on possible routes of travel.The consideration of such global battery charging or battery replacementrequirements of vehicles in a given area results in a more optimaldistribution of vehicles arriving at particular battery charging orreplacement stations thereby relieving overall congestion at suchstations.

An aspect of the present invention is that the artificial intelligencesystems and methods of the present inventions make use of existing routeguidance and navigation systems to derive potential routes of travel forfurther evaluation based on EV battery charging requirements. In thisway, the systems and methods of the present inventions simplify requiredcalculations for evaluation of potential routes wherein those potentialroutes have been derived taking in to account multiple route selectioncriteria including, for example, roadway conditions, traffic conditionsand congestion, weather conditions, police reported concerns, and otherconcerns as discussed above. Possible routes can be derived usingavailable navigation routing systems and methods. But those availableand useful route determination and routing systems and methods do notinclude consideration of EV battery charging requirements and theavailability of battery charging and/or battery replenishing stationsalong selected routes of travel. The present inventive systems andmethods augment those existing navigation and routing systems andmethods to further optimize route selection for dynamically changing EVbattery charging and/or replenishment requirements.

The artificial intelligence presentation of selected routes also greatlysimplifies the user interface to the route derivation for the EV batterycharging or replenishing system and methods of the present invention.Importantly, the simplified presentation of the route desirabilityrating indices and/or final route selection minimizes cognitivedistractions to the driver. Such cognitive distractions may increasedanger to the driver in the vehicle. Minimizing such distractions isclearly important for safety reasons.

In the above-described embodiment of artificial intelligence expertsystem derivation of preferred routes of travel, propositional calculusstatements provided by one or more experts form the basis of routeevaluation and selection. That information may be organized inmultidimensional matrices as discussed above for artificial intelligencealgorithmic evaluation and decision-making in desirable route selection.

In another embodiment of the present invention, the above describeddecision-making process may be augmented with the use of fuzzy logic. Itis clear from the above discussion that the estimated travel time andenergy needed parameter values will be variables with certain ranges ofuncertainty. As described below, artificial intelligence expert systemsusing of fuzzy logic are particularly well-suited in deriving controlrules for directing navigation of such vehicles with such uncertainty.It is to be understood that artificial intelligence expert system routederivation may be implemented without fuzzy logic as described above.The use of the above described expert defined propositional logic rulesmay be sufficient for some embodiments as described above. That said,fuzzy logic has found expanded uses in the development of sophisticatedcontrol systems. With this technology, complex requirements may beimplemented in amazingly simple, easily managed and inexpensivecontrollers. It is a relatively simple method of representing analogprocesses on a digital computer. It has been successfully applied in amyriad of applications such as flight control systems, camera systems,antilock brakes systems, washing machines, elevator controllers,hot-water heaters, and stock trading programs.

The intelligent system matrix (700) of FIG. 7 and its associatedpropositional logic expressions may be used to formulate a fuzzy logicimplementation of the electric vehicle control unit processing device(400) and/or cloud control unit (500) of FIGS. 4 and 5.

With fuzzy logic control, statements are written in the form of thepropositional logic statements as illustrated above. These statementsrepresent somewhat imprecise ideas reflecting the states of thevariables. The variable ranges for energy needed and travel timeindicated in FIG. 7 may be “fuzzified” as fuzzy logic variablesextending over the defined overlapping ranges as shown, for example, inFIG. 9. Fuzzy logic systems make use of “fuzzifers” that convert inputvariables into their fuzzy representations. “Defuzzifiers” convert theoutput of the fuzzy logic process into “crisp” numerical values that maybe used in system control.

For example, the graph (901) of FIG. 9 illustrates such a possible“fuzzification” for the energy needed index variable with overlappingranges indicated in the figure. In this example, on a scale of 1 to 10,the normalized energy needed for a particular route is rated at 8.5.Normalization may be accomplished, for example, by comparison to themaximum energy available from the EV battery source. As illustrated inthe FIG. 9, an energy needed rating of 8.5 results in a degree ofmembership (DOM) of 0.70 in the membership class “high.” In thisparticular example, the energy needed rating of 8.5 does not result inmembership in any other of the possible membership classes.

In a similar way, in the graph (902) of FIG. 9 “fuzzification” of thetravel time variable is illustrated. On a scale of 1 to 10, a normalizedtravel time value of 4.5 results in a DOM of 0.6 in the travel time“medium” membership class and 0.15 in the “low” membership class. Inthis case, for example, normalization may be accomplished by comparisonto a user defined maximum allowable travel time.

These DOM values may in turn be used in the fuzzy logic implementationto derive a defined, “crisp” numerical value for a route advisory actionindex. For example, in the above example of FIG. 9, the twopropositional logic statements “fire” as follows:

1. If energy needed is high and the travel time is medium then routedesirability rating is low.

2. If energy needed is high and travel time is low then the routedesirability rating is very low.

The conjunctive relation “and” corresponds to the logical intersectionof the two sets corresponding to the energy and distance variables. Inthis case the appropriate DOM is the minimum DOM for each of the sets atthe specified time. This is expressed algebraically as follows:(A∩B)(x)=min(A(x), B(x)) for all x∈X

Premises connected by an “OR” relationship are combined my taking thelarger DOM for the intersection values. This is expressed algebraicallyas follows:(A∪B)(x)=max(A(x), B(x)) for all x∈X

In the case of the exemplary propositional logic equations above: “Ifenergy needed is high and travel time is medium then route desirabilityrating is low.” The conjunctive relation “and” requires the use of theminimum value of the respective DOM's. From the graphs (901) and (902),for these propositional logic equations the corresponding DOM's are 0.7for the energy needed variable and 0.6 for the travel time variable.Correspondingly, consider the second propositional logic equation above:“If energy needed is high and travel time is low, then routedesirability rating is low.” In this case the corresponding DOM is 0.15for the travel time variable.

These values may be used to defuzzify the route advisory index degree ofmembership. As shown in (903) of FIG. 9, fuzzy ranges for the routeadvisory index may be defined in a similar manner to the energy neededand travel time variables. A numerical “crisp” value for the advisoryaction index can now be derived using defuzzification procedures. Asshown in FIG. 9, the DOM ranges for the route advisory index are cappedat values corresponding to the above analysis for the DOMs of the energyneeded and travel time variables. The final “crisp” numerical value ofthe route advisory index may, for example, be calculated based on thecentroid of the geometric figure for the DOM ranges of the graph (903)of FIG. 9. This calculation may be carried out by dividing the geometricfigure of FIG. 9 into sub-areas A_(i) with individual centroids x_(i)from the following formula.

$x_{c} = {( {\sum\limits_{i = 1}^{n}\;{x_{i}A_{i}}} )/( {\sum\limits_{i = 1}^{n}\; A_{i}} )}$

The result of such a calculation is shown in FIG. 9 yielding a routeadvisory index numerical value of 6.2.

While, for simplicity, the above example dealt with only two variables,namely user level of energy needed and travel time indices, the methoddescribed above may be expanded to more than two variables.

As discussed above, a calculation of the route advisory index may bemade for several possible routes corresponding to battery replenishingstations in the vicinity of the electric vehicle. For example, separatecalculations may be made for each of the routes A, B and C of FIG. 3 asdiscussed above. The route with the lowest route advisory index is thenchosen for recommendation to the driver of the electric vehicle. Lowerenergy needed and/or lower route travel times will yield lower routeadvisory indices corresponding to more preferred routes. Such a decisionmay be displayed by the display of control unit 400 of FIG. 4. Thecontrol unit may also provide navigation information to the electricvehicle to guide it along the selected route according to well-knownvehicle direction routing systems and methods.

FIG. 10 illustrates a route selection process for the above describedtwo input energy needed and travel time parameters for multiple routes.Parameters (1003) are input to the fuzzy control route selection (1001)for route 1 as indicated in the figure. Similarly, energy needed andtravel time parameters for the N^(th) route are input to the fuzzycontrol route calculation (1002) for route N, as shown in FIG. 10. Theoutputs from the multiple fuzzy logic control route selectioncalculations are input to the EV route and battery charger-replacementstation selection units (1005) and (106), where the most desirable routeis chosen. The driver of the electric vehicle is then notified of theroute and battery charger-replacement station selection at operation(1007).

FIG. 11 illustrates similar operation (1100) with three input variablesat (1103) and (1004) for each route, including energy needed, distanceto the destination and the travel time required to arrive at thedestination. In this case, three input variables are used in the fuzzylogic control route selections at (1101) and (1102) with the selectionof the best route at operation (1105) and (1106). Here again, the driverof the electric vehicle is notified of the route and batteryreplenishing station selection at (1107). Navigation along the selectedroute may commence as described above.

FIG. 12 illustrates in more detail exemplary fuzzy logic operationexecution (1200) by the device control unit (400) for the system andmethods of this invention. As shown in FIG. 12, these operations offuzzy logic inference engine (1201) include access to the artificialintelligence expert system knowledge base (1206) which may include thefuzzy logic rules discussed above. The fuzzy logic operations includethe fuzzifier (1203) used to establish degree of memberships (DOMs) asdiscussed above. The outputs of fuzzifier (1203) are fed to the fuzzylogic processing element (1204). Defuzzifier (1205) provides crispnumerical outputs for the route advisory index (1207) to the EV controlunit as discussed above.

Although the embodiments above have been described in considerabledetail, numerous variations and modifications will become apparent tothose skilled in the art once the above disclosure is fully appreciated.For example, embodiments with more or fewer variables to be analyzed asdescribed above are possible. Variations of the artificial intelligenceexpert system analysis may be used including embodiments that do not usefuzzy logic. Embodiments for the EV control unit as described above maybe integrated in various degrees with other motor vehicle telematics orsystem control processors and sensor systems. In some embodiments, theEV control unit may include only a subset of the capabilities discussedabove. In some embodiments, the EV control unit may include additionalcapabilities not shown herein. While the above disclosure is based on astandard EV vehicles, the same teachings set forth herein may be appliedto other vehicles such as trucks, buses, military vehicles, emergencyvehicles such as fire trucks and ambulance and the like. It is intendedthat the following claims be interpreted to embrace all such variationsand modifications.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. A method for routing anElectric Vehicle (EV) from a current position to a destination whereinsaid method comprises one or more specifically programmed computermachines with artificial intelligence expert system battery energymanagement and route selection optimization control, said method furthercomprising: a step of storing in electronic memory of said one or morespecifically programmed computer machines artificial intelligence expertsystem software program code for battery energy management and routeselection optimization control, said software program code including;battery energy and route selection optimization parameter definitionsincluding range of parameter values and subsets of those defined ranges;expert system propositional logic statements defining relationshipsbetween said battery energy parameters and route selection optimizationparameters based on parameter membership in said subset ranges; a stepof storing in in electronic memory of said one or more specificallyprogrammed computer machines one or more of the following: EVdescriptive information, EV energy requirements, EV batteryspecification information, and EV current position and the location ofthe destination of said EV; a step of monitoring and storing inelectronic memory of said one or more specifically programmed computermachines the status of said EV stored battery energy; a step ofexecuting said program code of said one or more specifically programmedcomputer machines with artificial intelligence expert system batteryenergy management and route selection optimization control comprising: astep of comparing current EV stored battery energy to one or moredefined thresholds; a step of transmitting information from said EV toone more cloud or remote computer/data processing systems when saidbattery energy is less than a selected threshold, wherein saidtransmitted information comprises one or more of the following: EVdescriptive information, EV energy requirements, EV batteryspecification information, EV stored battery energy status, and EVcurrent GPS position and the EV destination address location; a step ofsaid EV receiving artificial intelligence expert system route selectionoptimization information from said one or more cloud or remotecomputer/data processing systems for potential routes of travel, whereinsaid received route selection optimization information comprises:information regarding potential routes of travel for said EV to reachone or more battery charging or replacement stations and, after chargingor replacement, to continue on to said destination; informationregarding one or more route selection optimization parameters for eachof said potential routes; a step of artificial intelligence expertsystem evaluation of a potential route of travel by one or morespecifically programmed computer machines with artificial intelligenceexpert system battery energy management and route selection optimizationcontrol based at least in part on route selection optimization parametermembership in defined parameter subsets and artificial intelligenceexpert system propositional logic statements; and, a step of artificialintelligence expert system selection of a particular route of travel byone or more specifically programmed computer machine with artificialintelligence expert system battery energy management and route selectionoptimization control based at least in part on comparisons of resultsfrom said individual route evaluations of potential routes of travelbased on said received information.
 2. The method of claim 1 whereinsaid steps of artificial intelligence expert system evaluation andselection of a particular route of travel are executed by one or morespecifically programmed computer machines located in the EV withartificial intelligence expert system battery energy management androute selection optimization control.
 3. The method of claim 1 whereinsaid steps of artificial intelligence expert system evaluation andselection of a particular route of travel are executed by one or morespecifically programmed cloud based or remote computer/data processingsystems with artificial intelligence expert system battery energymanagement and route selection optimization control.
 4. The method ofclaim 1 wherein said transmitted EV descriptive information comprisesone or more of the following: vehicle type; vehicle loaded weight; and,vehicle energy requirement history.
 5. The method of claim 1 whereinsaid transmitted EV battery specification information comprises one ormore of the following: battery type; battery capacity; battery chargingrequirements; battery age; and, battery charging time.
 6. The method ofclaim 1 wherein said route selection optimization parameters define foreach EV potential route of travel the expected total travel time fromthe EV current location to the destination including intermediatebattery charging or replacement times and the total expected energyrequired to travel from the current position to the desired destination.7. The method of claim 6 wherein said EV total travel time for eachpotential route includes route roadway considerations including at leastone of roadway conditions, traffic congestion, weather conditions and/oremergency traffic considerations.
 8. The method of claim 7 wherein saidEV route selection optimization information further includesconsideration of actual or probable requests for route including batterycharging or replacement station usage from other EVs traveling within adefined radius or distance from said EV position.
 9. The method of claim1 wherein said EV is a self-driving vehicle.
 10. The method of claim 9wherein said system software program code for battery energy managementand route selection optimization control comprises expert systemartificial intelligence code with no required driver input for routedecision making.
 11. A method for routing an Electric Vehicle (EV) froma current position to a destination wherein the said method comprisesone or more specifically programmed computer machines with artificialintelligence expert system fuzzy logic battery energy management androute selection optimization control, said method comprising: a step ofstoring in electronic memory of said one or more specifically programmedcomputer machines artificial intelligence expert system fuzzy logicsoftware program code for battery energy management and route selectionoptimization control; a step of storing in in electronic memory of saidone or more specifically programmed computer machines EV descriptiveinformation, EV energy requirements, EV battery specificationinformation, the current position of said EV and the location of thedestination of said EV; a step of monitoring and storing in electronicmemory of said one or more specifically programmed computer machinesstatus of said EV stored battery energy; a step of executing saidprogram code of said one or more specifically programmed computermachines with artificial intelligence expert system fuzzy logic batteryenergy management and route selection optimization control comprising: astep of comparing current EV stored battery energy to defined thresholdsto estimate sufficiency of said stored energy to reach said destination;a step of transmitting information from said EV to one more cloud orremote computer/data processing systems when said battery energy is lessthan a selected threshold, wherein said transmitted informationcomprises: EV descriptive information, EV battery specificationinformation, EV energy requirements, EV stored battery energy status,and EV current position and the destination location; a step ofreceiving artificial intelligence expert system fuzzy logic derivedroute selection optimization information for potential routes of travelfrom said one or more cloud or remote computer/data processing system,wherein said received route guidance information comprises: informationregarding potential routes of travel for said EV to reach one or morebattery charging or replacement stations and, after battery charging orreplacement, to continue on to said destination; information regardingone or more route selection optimization parameters for each of saidpotential routes; a step of artificial intelligence expert system fuzzylogic selection of a particular route of travel based at least in parton said received information.
 12. The method of claim 11 wherein saidsteps of artificial intelligence expert system fuzzy logic evaluationand selection of a particular route of travel are executed by one ormore specifically programmed computer machines located in the EV withartificial intelligence expert system fuzzy logic battery energymanagement and route selection optimization control.
 13. The method ofclaim 12 wherein said steps of artificial intelligence expert systemfuzzy logic evaluation and selection of a particular route of travel areexecuted by one or more specifically programmed cloud based or remotecomputer/data processing system computer machines with artificialintelligence expert system fuzzy logic battery energy management androute selection optimization control.
 14. The method of claim 11 whereinsaid route evaluation criteria includes relative predictions ofconsidered route travel parameters including energy required by said EVto travel to said destination and travel time of said EV to saiddestination.
 15. The method of claim 14, wherein said relativepredictions comprise defined fuzzy sets with possible overlappingparameter ranges and further wherein said artificial intelligence expertsystem fuzzy logic decisions are based on calculation of a degree ofmembership in defined fuzzy sets for particular considered routeevaluation parameters.
 16. The method of claim 15 further comprising astep of defuzzifying multiple fuzzy logic degree of membership resultsto derive crisp numerical route selection indices values for particularroutes considered.
 17. The method of claim 16 further comprisingselecting a particular recommended route of travel from among multiplesuch potential routes by comparing said derived crisp numerical routeselection indices values for considered routes.
 18. A method for routinga driverless Electric Vehicle (EV) from a current position to adestination wherein said method comprises one or more specificallyprogrammed computer machines with artificial intelligence expert systembattery energy management and route selection optimization control, saidmethod further comprising: a step of storing in electronic memory ofsaid one or more specifically programmed computer machines artificialintelligence expert system software program code for battery energymanagement and route selection optimization control, said softwareprogram code including; battery energy and route selection optimizationparameter definitions including range of parameter values and subsets ofthose defined ranges expert system propositional logic statementsdefining relationships between said battery energy parameters and routeselection optimization parameters based on parameter membership in saidsubset ranges; a step of storing in in electronic memory of said one ormore specifically programmed computer machines one or more of thefollowing: EV descriptive information, EV energy requirements, EVbattery specification information, and EV current position and thelocation of the destination of said EV; a step of monitoring and storingin electronic memory of said one or more specifically programmedcomputer machines the status of said EV stored battery energy; a step ofexecuting said program code of said one or more specifically programmedcomputer machines with artificial intelligence expert system batteryenergy management and route selection optimization control comprising: astep of comparing current EV stored battery energy to one or moredefined thresholds; a step of transmitting information from said EV toone more cloud or remote computer/data processing systems when saidbattery energy is less than a selected threshold, wherein saidtransmitted information comprises one or more of the following: EVdescriptive information, EV energy requirements, EV batteryspecification information, EV stored battery energy status, and EVcurrent GPS position and the EV destination address location; a step ofsaid EV receiving artificial intelligence expert system derived routeselection optimization information from said one or more cloud or remotecomputer/data processing systems for potential routes of travel, whereinsaid received route selection optimization information comprises:information regarding potential routes of travel for said EV to reachone or more battery charging or replacement stations and, after chargingor replacement, to continue on to said destination; informationregarding one or more route selection optimization parameters for eachof said potential routes; a step of artificial intelligence expertsystem evaluation of a particular route of travel by one or morespecifically programmed computer machines with artificial intelligenceexpert system battery energy management and route selection optimizationcontrol based at least in part on said received information, routeselection optimization parameter membership in defined parameter subsetsand artificial intelligence expert system propositional logicstatements; and, a step of artificial intelligence expert systemselection of a particular route of travel by one or more specificallyprogrammed computer machines with artificial intelligence expert systembattery energy management and navigation route selection optimizationcontrol based at least in part on comparisons of results from saidindividual route evaluations of potential routes of travel based on saidreceived information, whereby said driverless EV is guided along anartificial intelligence expert system selected route chosen from one ormore other potential routes based on defined route selectionoptimization criteria without requiring additional EV driver input orcontrol actions.
 19. The method of claim 18 wherein said steps ofartificial intelligence expert system evaluation and selection of aparticular route of travel are executed by one or more specificallyprogrammed computer machines located in the EV with artificialintelligence expert system battery energy management and route selectionoptimization control.
 20. The method of claim 18 wherein said steps ofartificial intelligence expert system evaluation and selection of aparticular route of travel are executed by one or more specificallyprogrammed cloud based or remote computer/data processing systemcomputer machines with artificial intelligence expert system batteryenergy management and route selection optimization control.